Introduction
In Python, a lambda function is a small, anonymous function that is defined using the lambda keyword. These functions are called anonymous function because they don't have a name like a regular function defined with the def keyword. Lambda functions are particularly useful when you need a short, disposable function for a specific task.
Syntax
lambda arguments: expression
In the lambda function syntax, arguments represent the input parameters of the function, and expression represent the operation or calculation to be performed on those arguments. The result of the expression is implicitly returned by the lambda function.
Let's start with a basic example to understand how lambda functions work:
# Regular function
def square(x):
return x ** 2
print(square(5))
# Output: 25
#lambda function
square = lambda x: x ** 2
print(square(5))
# Output: 25
In the above example, we define a lambda function square that takes one argument x and returns the square of x. We then call the square function with the argument 5, and it prints the result 25.
Lambda functions are often used with built-in functions like map(), filter(), and reduce(). These functions take a function as one of their arguments, making lambda functions a perfect fit.
Using map() with a Lambda Function
The map() function applies a given function to each item of an iterable (list, tuple) and returns a map object containing the results.
numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x ** 2, numbers))
print(squared_numbers)
# Output: [1, 4, 9, 16, 25]
In above example, i have used a lambda function lambda x: x ** 2 to square each number in the numbers list. The map() function applies this lambda function to each item in the list, and the resulting squared values are stored in the squared_numbers list.
Using filter() with a Lambda Function
The filter() function is used to filter elements from an iterable (lists, tuples, or sets) based on a function that returns a boolean value (True or False). The elements for which the function returns True are included in the final result, while those for which the function returns False are excluded from the final result.
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)
# Output: [2, 4, 6, 8, 10]
In above example, i have used a lambda function lambda x: x % 2 == 0 to check if a number is even. The filter() function applies this lambda function to each item in the numbers list and creates a new list even_numbers containing only the even numbers.
Using reduce() with a Lambda Function
The reduce() function applies a function of two arguments simultaneously to the elements of a sequence, from left to right, to reduce the sequence to a single value and return a single value as output. reduce() function is part of the functools module.
from functools import reduce
numbers = [1, 2, 3, 4, 5]
sum_of_numbers = reduce(lambda x, y: x + y, numbers)
print(sum_of_numbers)
# Output: 15
In above example, i have used reduce(lambda x, y: x + y, numbers) to calculate the sum of all numbers in the numbers list. The lambda function lambda x, y: x + y takes two arguments, x and y, and returns their sum. The reduce() function applies this lambda function simultaneously to the elements of the numbers list, starting with the first two elements (1 and 2), then using the result (3) and the next element (3), and so on, until all elements have been processed. The final result is the sum of all numbers, 15.
Summary
Lambda functions are a powerful tool in Python that allow you to write small and efective code. Lambda functions are particularly useful when working with functional programming concepts and higher-order functions. While lambda functions can make simple operations more concise, they should be used carefully. Too many lambda functions or lambda functions for complex operations can make your code harder to read and understand. It's generally recommended to use regular functions defined with the def keyword for complex operations or reusable code.